Multimodality imaging of COVID-19 pneumonia: from diagnosis to follow-up. A comprehensive review.

TL;DR: A review of the technologies used to detect SARS-CoV-2 in clinical laboratories as well as advances made for molecular, antigen-based, and immunological point-of-care testing, including recent developments in sensor and biosensor devices is presented in this paper.

TL;DR: In this article, a review of the diagnostic strategies available for the diagnosis of COVID-19 infection in both clinical and research settings is presented, in which the technical and instrumental characteristics of diagnostic methods used are described.

TL;DR: It is believed that the wide availability of high-quality, curated, and benchmarked COVID-19 imaging data sets offers the great promise of a transformative test bed to develop, validate, and disseminate novel deep learning methods in the frontiers of data science and artificial intelligence.

TL;DR: During the first 2 months of the current outbreak, Covid-19 spread rapidly throughout China and caused varying degrees of illness, and patients often presented without fever, and many did not have abnormal radiologic findings.

TL;DR: Characteristics of patients who died were in line with the MuLBSTA score, an early warning model for predicting mortality in viral pneumonia, and further investigation is needed to explore the applicability of the Mu LBSTA scores in predicting the risk of mortality in 2019-nCoV infection.